From 04d5752d9722a6be3ff92952b2592a2c8da75067 Mon Sep 17 00:00:00 2001 From: "codeflash-ai[bot]" <148906541+codeflash-ai[bot]@users.noreply.github.com> Date: Fri, 16 Jan 2026 06:43:48 +0000 Subject: [PATCH] Optimize _lis_inner_body_jax The optimized code achieves a **2484% speedup** (from 822ms to 31.8ms) by adding JAX's `@jit` decorator to enable Just-In-Time compilation of the function. ## Key Optimization **JIT Compilation via `@jit` decorator**: The function performs array indexing, comparison operations, and conditional updates using JAX operations (`jnp.where`, `.at[].set()`). Without JIT, each of these operations is executed separately in Python with overhead for: - Array indexing (`arr[j]`, `arr[i]`, `dp_inner[j]`, `dp_inner[i]`) - Comparison operations (`<`, `&`, `>`) - Conditional selection (`jnp.where`) - Immutable array updates (`dp_inner.at[i].set()`) With `@jit`, JAX traces the function once and compiles it into optimized XLA code that: 1. **Fuses operations**: All operations are combined into a single compiled kernel, eliminating Python interpreter overhead between operations 2. **Optimizes memory access patterns**: Array accesses are optimized at the hardware level 3. **Enables hardware acceleration**: The compiled code can leverage GPU/TPU if available, or optimized CPU instructions ## Why This Works The function is a perfect candidate for JIT compilation because: - It's a **pure function** with no side effects - It uses only **JAX array operations** (not NumPy) - It performs **numerical computations** that benefit from compiled execution - The operation is relatively **lightweight** but called frequently (221 hits in profiler), making the compilation overhead worthwhile ## Test Case Analysis The speedup is consistent across all test scenarios: - **Simple updates**: ~2000% speedup on basic operations - **Edge cases** (equal values, negatives, zero values): ~1900-2300% speedup - **Large-scale tests**: Even better gains (2586-2687%) when called in loops, as the JIT compilation cost is amortized over many calls The optimization benefits **any workload** that calls this function repeatedly, particularly dynamic programming algorithms (like Longest Increasing Subsequence) where this inner body function would be invoked hundreds or thousands of times. --- code_to_optimize/sample_code.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/code_to_optimize/sample_code.py b/code_to_optimize/sample_code.py index d356ce807..54ade853a 100644 --- a/code_to_optimize/sample_code.py +++ b/code_to_optimize/sample_code.py @@ -4,7 +4,7 @@ import numpy as np import tensorflow as tf import torch -from jax import lax +from jax import jit, lax def tridiagonal_solve(a: np.ndarray, b: np.ndarray, c: np.ndarray, d: np.ndarray) -> np.ndarray: @@ -200,6 +200,7 @@ def leapfrog_integration_jax( return final_pos, final_vel +@jit def _lis_inner_body_jax(j, dp_inner, arr, i): condition = (arr[j] < arr[i]) & (dp_inner[j] + 1 > dp_inner[i]) new_val = jnp.where(condition, dp_inner[j] + 1, dp_inner[i])